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Purpose -Jacquard woven fabrics are widely used in various sections of upholstery industry, where mattress cover is one of them. Strength of jacquard woven mattress fabric depends on several factors. The objective of this study is to model the multi-linear relationship between fibre, yarn and fabric parameters on the strength of fabric using artificial neural network (ANN) and Taguchi design of experiment (TDOE) methodologies. Design/methodology/approach -TDOE was applied to determine the optimum design values and the contribution of each parameter. Robustness (performance) of models is measured by root mean squared error (RMSE). These tools will enable the user to predict the fabric strength from number of given inputs. It also provides the knowledge related to the contribution of fibre, yarn and fabric parameters on fabric strength. Fabrics tested in this study made from different fibre types and max/min level for several fabric and yarn-related parameters. The models generated with TDOE and ANN methodologies were compared with the actual experimental data. Findings -It was found that ANN model gives better approximation with the minimum RMSE.Research limitations/implications -The data taken from factory are related with jacquard woven fabric.Practical implications -This study has many practical implications that brings up a general approach for textile industry. During manufacturing, waste or scrap ratio can be reduced and production planning become more efficient. Originality/value -Firstly, before starting manufacturing in factory, we can easily predict the strength of woven fabric using the defined factors. This makes the model usable at the planning stage of the fabric. Secondly, the contribution of factors affecting fabric strength was determined. The ANN model generated in this study helps the engineers of planning department at the company easy to plan the manufacturing of fabric with a good estimation of fabric strength before the production order.

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Multi-period single-item lot sizing problem under stochastic environment has been tackled by few researchers and remains in need of further studies. It is mathematically intractable due to its complex structure. In this paper, an optimum lot-sizing policy based on minimum total relevant cost under price and demand uncertainties was studied by using various artificial neural networks trained with heuristic-based learning approaches; genetic algorithm (GA) and bee algorithm (BA). These combined approaches have been examined with three domainspecific costing heuristics comprising revised silver meal (RSM), revised least unit cost (RLUC), cost benefit (CB). It is concluded that the feed-forward neural network (FF-NN) model trained with BA outperforms the other models with better prediction results. In addition, RLUC is found the best operating domain-specific heuristic to calculate the total cost incurring of the lot-sizing problem. Hence, the best paired heuristics to help decision makers are suggested as RLUC and FF-NN trained with BA.Keywords: Stochastic lot-sizing, Feed-Forward Neural Networks, Bee Algorithm, Genetic Algorithms, Taguchi methods. IntroductionLot-sizing problems have been studied with various respects for a long time whilist keeping the emphasis on modelling and optimisation of deterministic versions, which are known with the NP-Hard nature [1,2,3] and usually handled with heuristic methods in-line with WagnerWhitin (WW) approach [4,5,6]. On the other hand, the real world versions of these problems are not as static and deterministic as modelled and handled in these ways, but, are rather dynamic and subject to probabilistic processes. That makes the problem type hard to model in easily solvable mathematical structures due to the complexity and uncertainty issues.The stochastic dynamic lot-sizing (SDLS) problem can be formulated, in analytical or simulation models, either by assuming a penalty cost for each stock out and unsatisfied demand or by minimising the ordering and inventory costs subject to satisfying some customer service-level criterion. The analytical modelling approach is most frequently encountered in particular stochastic programming, where these models tackle only one type of uncertainty and assume a simple production system structure. Exact analytical solutions can only be developed, when the model is adequately simple. Further numbers of uncertain inputs/parameters escalate the complexity of the problems to an un-handlable state with analytical models. Due to the limitations given rise by stochastic/uncertain nature of controlling parameters in dynamic lot sizing problem (DLSP), heuristic approaches are preferred, rather than analytical models, in the solving larger-scale DLSP instances.

Webbings are used in parachute assemblies as reinforcing units for the strength they provide. The strength of these seams is an important characteristic which has a substantial influence on the mechanical property of the parachute assemblies. It is well established that factors such as fabric width, folding length of joint, seam design and seam type will all have an impact on seam strength. In this work, the effect of these factors on seam strength was studied using both Taguchi's design of experiment (TDOE) as well as an artificial neural network (ANN). In TDOE, two levels were chosen for the factors mentioned above. An L8 design was adopted and an orthogonal array was generated. The contribution of each factor to seam strength was analyzed using analysis of variance (ANOVA) and signal to noise ratio methods. From the analysis it was found that the fabric width, folding length of joint and interaction between the folding length of joint and the seam design affected seam strength significantly. Further, using TDOE, an optimal configuration of levels of factors was found. In order to contrast and compare the results from TDOE, an ANN was also used to predict seam strength using the above mentioned factors as inputs. The prediction from TDOE and ANN methodologies were compared with physical seam strength. It was established from these comparisons, in which the root mean square error was used as an accuracy measure, that the predictions by ANN were better in accuracy than those predicted by TDOE.